{"id":"W3033414571","doi":"10.3390/rs12111850","title":"Assessing the Operation Parameters of a Low-altitude UAV for the Collection of NDVI Values Over a Paddy Rice Field","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; China Agricultural University; South China Agricultural University; National Natural Science Foundation of China","keywords":"Normalized Difference Vegetation Index; Environmental science; Remote sensing; Paddy field; Altitude (triangle); Vegetation Index; Low altitude; Scale (ratio); Agricultural engineering; Mathematics; Leaf area index; Geography; Agronomy; Cartography; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007262487,0.0004585093,0.0002239058,0.0002775897,0.0003237428,0.000482312,0.0002620461,0.0003569438,0.0003667337],"category_scores_gemma":[0.001668452,0.0001156533,0.0002200469,0.0002733837,0.0001802688,0.0004400151,0.0001848323,0.0002064563,0.0001039309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004227867,"about_ca_system_score_gemma":0.0003394301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005240912,"about_ca_topic_score_gemma":0.007022331,"domain_scores_codex":[0.999744,0.00006113911,0.00002121403,0.00005899585,0.00006548081,0.00004917883],"domain_scores_gemma":[0.9989395,0.0004946846,0.0001228232,0.0001036636,0.0002697151,0.00006972689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001802833,0.0009104529,0.2190375,0.0005922877,0.0001788006,0.0005030039,0.0003608187,0.1009657,0.6137462,0.0001849318,0.0004990032,0.06121849],"study_design_scores_gemma":[0.00009200785,0.004371863,0.5641782,0.00004749868,0.0003216422,0.0002517352,0.0007289691,0.1576933,0.2706287,0.0001275282,0.001471507,0.00008703571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965109,0.00007239862,0.002917496,0.00001953116,0.000006206325,0.00003165762,0.0001469801,0.00003984304,0.0002549549],"genre_scores_gemma":[0.9967209,0.0000388905,0.003069628,0.000007395857,0.000001250224,0.00001692,0.00008899425,0.000006622955,0.00004935433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005240912,"threshold_uncertainty_score":0.0104208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02641285521215851,"score_gpt":0.2914527911797748,"score_spread":0.2650399359676163,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}